John Langford


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Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
Dipendra Misra | John Langford | Yoav Artzi
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of separately trained models, we learn a single model to jointly reason about linguistic and visual input. We use reinforcement learning in a contextual bandit setting to train a neural network agent. To guide the agent’s exploration, we use reward shaping with different forms of supervision. Our approach does not require intermediate representations, planning procedures, or training different models. We evaluate in a simulated environment, and show significant improvements over supervised learning and common reinforcement learning variants.


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Hands-on Learning to Search for Structured Prediction
Hal Daumé III | John Langford | Kai-Wei Chang | He He | Sudha Rao
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts